This looks like a REALLY cool tool for tech-focused startups. I like that its not just resolving the stripe bloat but also the code. You can setup a full production-ready payments processor without worrying about the code.

I'm wondering - is there some overhead required to keep operations up? Or Flowglad takes care of everything between code and compliance

Also, sick sweater I have the same one XD

This isn't quite a boilerplate to start your own payment processor - but an open source payments provider which you does the entire job to be done: handling the flow of both money and value inside your app. And yes, for now your coding agent + our MCP and SDKs handle the code. Our API handles the state management of entitlements usage credits etc. This is the really messy part that hasn't been well addressed yet by incumbents.

And then we, alongside the processing partners we use on the backend (currently Stripe, soon hopefully beyond) handle the rest.

Re the sweater: great minds ;)

I built a food delivery price optimization platform that algorithmically stacks discounts across multiple layers (store promos, credit card rewards, gift cards, etc). Initially, I found a lot of discounts were hiding behind the massive marketing budgets of delivery corporations. I originally architected cross-platform price comparison, but delivery platforms' terms of service explicitly prohibit displaying "priced items from multiple retailers on the same screen." This forced a complete pivot from comparison to single-platform optimization. Basically they don't want me comparing their prices and promos to competitors. Makes sense.

So my adapted approach is: Instead of price comparison, the system takes your list/search terms and builds optimized platform-specific carts.

- Real-time promotion discovery - Credit card reward optimization - Smart cart building so it can qualify for all discounts - Uses ML to suggest product swaps that maintain savings while improving nutritional value - Predictive rebate matching based on shopping patterns

In early testing I'm seeing around 35% higher cart values on average. So I really think the idea has legs and I don't want to give up, but navigating these partnership dynamics as a solo founder is proving to be challenging.

Has anyone else had to completely restructure their product architecture due to partnership requirements? How do you balance technical capabilities with business partnership limitations?

I built a food delivery price optimization platform that algorithmically stacks discounts across multiple layers (store promos, credit card rewards, gift cards, etc).

Initially, I found a lot of discounts were hiding behind the massive marketing budgets of delivery corporations. I originally architected cross-platform price comparison, but delivery platforms' terms of service explicitly prohibit displaying "priced items from multiple retailers on the same screen." This forced a complete pivot from comparison to single-platform optimization. Basically they don't want me comparing their prices and promos to competitors. Makes sense.

So my adapted approach is: Instead of price comparison, the system takes your list/search terms and builds optimized platform-specific carts.

- Real-time promotion discovery - Credit card reward optimization - Smart cart building so it can qualify for all discounts - Uses ML to suggest product swaps that maintain savings while improving nutritional value - Predictive rebate matching based on shopping patterns

In early testing I'm seeing around 35% higher cart values on average. So I really think the idea has legs and I don't want to give up, but navigating these partnership dynamics as a solo founder is proving to be challenging.

Has anyone else had to completely restructure their product architecture due to partnership requirements? How do you balance technical capabilities with business partnership limitations?